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An ELM based multi-agent system and its applications to power generation

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dc.citedby2
dc.contributor.authorYaw C.T.en_US
dc.contributor.authorWong S.Y.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorYap H.J.en_US
dc.contributor.authorAmirulddin U.A.U.en_US
dc.contributor.authorTan S.C.en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid55812054100en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid35319362200en_US
dc.contributor.authorid26422804600en_US
dc.contributor.authorid7403366395en_US
dc.date.accessioned2023-05-29T06:57:03Z
dc.date.available2023-05-29T06:57:03Z
dc.date.issued2018
dc.descriptionBenchmarking; Electric circuit breakers; Knowledge acquisition; Learning systems; Neural networks; Pattern recognition; Power generation; Activation functions; Benchmark datasets; Certified belief in strength; Circulating water system; Extreme learning machine; ITS applications; Trust management; Trust measurement; Multi agent systemsen_US
dc.description.abstractThis paper presents an implementation of Extreme Learning Machine (ELM) in the Multi-Agent System (MAS). The proposed method is a trust measurement approach namely Certified Belief in Strength (CBS) for Extreme Learning Machine in Multi-Agent Systems (ELM-MAS-CBS). The CBS is applied on the individual agents of MAS, i.e., ELM neural network. The trust measurement is introduced to compute reputation and strength of the individual agents. Strong elements that are related to the ELM agents are assembled to form the trust management in which will be letting the CBS method to improve the performance in MAS. The efficacy of the ELM-MAS-CBS model is verified with several activation functions using benchmark datasets (i.e., Pima Indians Diabetes, Iris and Wine) and real world applications (i.e., circulating water systems and governor). The results show that the proposed ELM-MAS-CBS model is able to achieve better accuracy as compared with other approaches. � 2018 - IOS Press and the authors. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.3233/IDT-180325
dc.identifier.epage171
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85044389210
dc.identifier.spage163
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85044389210&doi=10.3233%2fIDT-180325&partnerID=40&md5=a45dd630d8d1d22f73d01a45ca0a0dfb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24214
dc.identifier.volume12
dc.publisherIOS Pressen_US
dc.sourceScopus
dc.sourcetitleIntelligent Decision Technologies
dc.titleAn ELM based multi-agent system and its applications to power generationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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